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  • 實戰 TensorFlow|Google 深度學習系統 黃文堅、唐源 著、蔡勝文 譯

    .深度剖析如何使用TecnsorFlow實現主流神經網路:  .AutoEncoder  .MLP  .CNN(AlexNet,VGGNet,Inception Net,ResNet)  .Word2Vec  .RNN(LSTM,Bi-RNN)  .Deep Reinforcement Learning(Policy Network、Value Network) .解說TensorBoard、多GPU平行、分布式平行等元件的使用方法 .TF.Learn從入門到精通,TF.Contrib詳解 Chapter 1 TensorFlow基礎  Chapter 2 TensorFlow和其他深度學習框架的比較 Chapter 3 TensorFlow第一步 Chapter 4 TensorFlow實作自編碼器及多層感知機 Chapter 5 TensorFlow實作卷積神經網路 Chapter 6 TensorFlow實作經典卷積神經網路 Chapter 7 TensorFlow實作遞迴神經網路及Word2Vec Chapter 8 TensorFlow實作深度強化學習 Chapter 9 TensorBoard、多GPU平行化及分散式平行化 Chapter 10 TF.Learn從入門到精通 Chapter 11 TF.Contrib的其他組件

    2019-05-26
    27
  • TensorFlow+Keras深度學習人工智慧實務應用 作者: 林大貴

    CHAPTER01 人工智慧、機器學習、深度學習介紹 CHAPTER02 深度學習的原理 CHAPTER03 TensorFlow與Keras介紹 CHAPTER04 在Windows安裝TensorFlow與Keras CHAPTER05 在Linux Ubuntu安裝TensorFlow與Keras CHAPTER06 Keras MNIST手寫數字辨識資料集介紹 CHAPTER07 Keras多元感知器(MLP)辨識手寫數字 CHAPTER08 Keras卷積神經網路(CNN)辨識手寫數字 CHAPTER09 Keras Cifar-10影像辨識資料集介紹 CHAPTER10 Keras卷積神經網路(CNN)辨識Cifar-10影像 CHAPTER11 Keras鐵達尼號旅客資料集介紹 CHAPTER12 Keras多層感知器(MLP)預測鐵達尼號旅客生存機率 CHAPTER13 IMDb網路電影資料集與自然語言處理介紹 CHAPTER14 Keras建立MLP、RNN、LSTM模型,進行IMDb情緒分析 CHAPTER15 TensorFlow程式設計模式介紹 CHAPTER16 以TensorFlow張量運算模擬神經網路運作 CHAPTER17 TensorFlow Mnist手寫數字辨識資料集介紹 CHAPTER18 TensorFlow多層感知器MLP辨識手寫數字 CHAPTER19 TensorFlow卷積神經網路CNN辨識手寫數字 CHAPTER20 TensorFlow GPU版本安裝 CHAPTER21 使用GPU加快TensorFlow與Keras訓練 附錄A 本書範例程式下載與安裝說明

    2019-05-26
    48
  • 實戰Google深度學習技術:使用TensorFlow 作者: 才雲科技 Caicloud, 鄭澤宇, 顧思宇

    第1章 深度學習簡介 在類似AlphaGo、無人駕駛汽車等最新技術的背後,深度學習是推動這些技術發展的核心力量。"深度學習"是本書的核心概念。透過閱讀本章,讀者將從多個角度瞭解這一概念。 第2章 TensorFlow環境搭建 本章將介紹如何安裝TensorFlow環境以及在安裝好的環境中執行簡單的TensorFlow範例程式。 第3章 TensorFlow入門 本章將詳細地介紹TensorFlow基本概念。在本章的前3節中,將分別介紹TensorFlow 的計算模型、資料模型和執行模型。透過這三個角度對TensorFlow的介紹,讀者可以對TensorFlow的工作原理有一個大致的瞭解。在本章的最後一節中,將簡單介紹神經網路的主要計算流程,並介紹如何透過TensorFlow實作這些計算。 第4章 深層神經網路 在這一章中,將進一步介紹如何設計和最佳化神經網路,使得它能夠更好地對未知的樣本進行預測。 第5章 MNIST數字識別問題 第4章介紹了訓練神經網路模型時需要考慮的主要問題以及解決這些問題的常用方法。這一章將透過一個實際問題來驗證第4章中介紹的解決方法。本章將使用的資料集是MNIST手寫體數字識別資料集。在很多深度學習課程中,這個資料集都會被當作第一個案例。在驗證神經網路最佳化方法的同時,本章也會介紹使用TensorFlow訓練神經網路的最佳實踐。 第6章 影像識別與卷積神經網路 本章將主要透過卷積神經網路在影像識別上的應用來講解卷積神經網路的基本原理以及如何使用TensorFlow實作卷積神經網路。 第7章 影像資料處理 本章將介紹如何對影像資料進行預處理使訓練得到的神經網路模型盡可能不被無關因素所影響。但與此同時,複雜的預處理過程可能導致訓練效率的下降。為了減小預處理對於訓練速度的影響,在本章中也將詳細地介紹TensorFlow中多執行緒處理輸入資料的解決方案。 第8章 遞迴神經網路 本章中將介紹另外一種常用的神經網路結構 — 遞迴神經網路以及遞迴神經網路中的一個重要結構— 長短時記憶網路(long short-term memory,LSTM)。本章也將介紹遞迴神經網路在自然語言處理(natural language processing,NLP)問題以及時序分析問題中的應用,並提供具體的TensorFlow程式來解決一些經典的問題。 第9章 TensorBoard視覺化 訓練神經網路十分複雜,有時需要幾天甚至幾周的時間。為了更好的管理、除錯和最佳化神經網路的訓練過程,TensorFlow提供了一個視覺化工具TensorBoard。本章將詳細介紹TensorBoard的使用方法。 第10章 TensorFlow計算加速 本章將介紹如何透過TensorFlow利用GPU或/和分散式運算進行模型訓練。

    2019-05-26
    11
  • ARM Cortex A9 嵌入式系統設計寶典 Base on Xilinx Zynq

    本書分為基礎篇與進階篇兩部分,基礎篇中介紹了Zynq器件、ZedBoard,並配有簡單入門實驗,同時針對軟體發展人員增設了FPGA硬體加速等內容。在進階篇中介紹了利用Zynq進行軟硬體協同設計,同時對處理器與可程式設計邏輯介面等技術進行了詳細剖析。 本書提供了20個詳細的設計案例,涵蓋了硬體板卡、FPGA邏輯、Linux驅動、Linux作業系統、上層應用、軟硬體協同設計等Zynq開發中可 能遇到的各個方面的知識,並在最後將前述獨立案例整合為4個系統案例。本書重點突出實戰,以案例為指導,配合介紹相關參考文檔,協助讀者儘快掌握在 Zynq上進行各項設計的方法。 本書可作為Zynq初學者、軟硬體協同設計開發人員的參考用書,亦可作為大專院校嵌入式系統設計、片上系統設計、可程式設計邏輯器件等相關專業的教師和學生的參考用書。

    2019-05-26
    9
  • Natural Language Processing in Action.pdf

    Natural Language Processing in Action is your guide to creating machines that understand human language using the power of Python with its ecosystem of packages dedicated to NLP and AI. About the Technology Recent advances in deep learning empower applications to understand text and speech with extreme accuracy. The result? Chatbots that can imitate real people, meaningful resume-to-job matches, superb predictive search, and automatically generated document summaries—all at a low cost. New techniques, along with accessible tools like Keras and TensorFlow, make professional-quality NLP easier than ever before.

    2019-05-14
    22
  • Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf

    Build practical applications of computer vision using the OpenCV library with Python. This book discusses different facets of computer vision such as image and object detection, tracking and motion analysis and their applications with examples. The author starts with an introduction to computer vision followed by setting up OpenCV from scratch using Python. The next section discusses specialized image processing and segmentation and how images are stored and processed by a computer. This involves pattern recognition and image tagging using the OpenCV library. Next, you’ll work with object detection, video storage and interpretation, and human detection using OpenCV. Tracking and motion is also discussed in detail. The book also discusses creating complex deep learning models with CNN and RNN. The author finally concludes with recent applications and trends in computer vision. After reading this book, you will be able to understand and implement computer vision and its applications with OpenCV using Python. You will also be able to create deep learning models with CNN and RNN and understand how these cutting-edge deep learning architectures work. What You Will Learn Understand what computer vision is, and its overall application in intelligent automation systems Discover the deep learning techniques required to build computer vision applications Build complex computer vision applications using the latest techniques in OpenCV, Python, and NumPy Create practical applications and implementations such as face detection and recognition, handwriting recognition, object detection, and tracking and motion analysis Who This Book Is For Those who have a basic understanding of machine learning and Python and are looking to learn computer vision and its applications.

    2019-05-14
    9
  • Machine Learning and AI for Healthcare.pdf

    Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things. What You'll Learn Gain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Select learning methods/algorithms and tuning for use in healthcare Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agents Who This Book Is For Health care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings.

    2019-05-14
    7
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